DBMS > EsgynDB vs. Google Cloud Bigtable vs. Hazelcast vs. Microsoft SQL Server vs. Riak KV

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. Hazelcast vs. Microsoft SQL Server vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A widely adopted in-memory data grid

Microsofts flagship relational DBMS

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Key-value store
Wide column store

Key-value store

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Graph DBMS
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

www.esgyn.cn

cloud.google.com/bigtable

hazelcast.com

www.microsoft.com/en-us/sql-server

Technical documentation

cloud.google.com/bigtable/docs

hazelcast.org/imdg/docs

learn.microsoft.com/en-US/sql/sql-server

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Esgyn

Google

Hazelcast

Microsoft

OpenSource, formerly Basho Technologies

Initial release

2015

2008

1989

2009

Current release

5.3.6, November 2023

SQL Server 2022, November 2022

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

restricted free version is available

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++, Java

Java

C++

Erlang

Server operating systems

Linux

hosted

All OS with a Java VM

Linux
Windows

Linux
OS X

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Event Listeners, Executor Services

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Erlang

Triggers

yes

Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

Replicated Map

yes, but depending on the SQL-Server Edition

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Atomic single-row operations

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Role-based access control

fine grained access rights according to SQL-standard

yes, using Riak Security

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